ai-memory is a shared long-term memory system for coding agents that preserves project knowledge, unfinished work, failed approaches, and open questions across tools and machines. It is used by individual developers and teams to hand work between different coding agents and continue projects without repeating the context.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add akitaonrails/ai-memory --skill ai-memory-handoffgit clone --depth 1 https://github.com/akitaonrails/ai-memoryWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/akitaonrails/ai-memory/ai-memory-handoff)<a href="https://agentmods.dev/skills/akitaonrails/ai-memory/ai-memory-handoff"><img src="https://agentmods.dev/badge/skills/akitaonrails/ai-memory/ai-memory-handoff/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/akitaonrails/ai-memory/ai-memory-handoff"><img src="https://agentmods.dev/badge/skills/akitaonrails/ai-memory/ai-memory-handoff.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00057 | $0.00772 |
| Opus 5 | $0.00028 | $0.00386 |
| Sonnet 5 | $0.00011 | $0.00154 |
| Haiku 4.5 | $0.00006 | $0.00077 |
Grade A, and why
ai-memory-handoff scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 45 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ai-memory handoff
Use this skill for single-use cross-session handoffs. Handoffs are for the next agent, not durable project documentation.
Tools in this cluster
memory_handoff_acceptconsumes the pending handoff when the user asks where we left off and no already-fetched handoff block is visible.memory_handoff_begincreates a terse next-session handoff only when the user is wrapping up, ending the session, or explicitly asks to save context for the next session.memory_handoff_cancelexpires a mistaken pending handoff by exact handoff id.
Single-use handoff behavior
The SessionStart hook usually fetches and consumes any pending handoff before the agent sees its first prompt. If the current context already contains a pending handoff block, answer from that block directly. Do not call the accept tool again to find it in another project, because handoffs are single-use and the tool will normally return null after SessionStart consumed it.
If no pending handoff block is visible and the user asks where we left off, then use the accept tool with the client-aware project scope below.
Creating a handoff
Create a handoff only at session end or when the user explicitly asks to save context for the next session. Do not use handoffs for status checks, briefings, project notes, or permanent memory. Keep the summary to two or three concise sentences, and put details in open questions and next steps bullets.
Lifecycle hooks already capture routine prompts and tool calls, so do not manually write a handoff just to record normal progress.
On a shared server, a handoff belongs to the operator who created it. Set shared: true only when the user explicitly wants any operator in the project to receive the baton; do not infer sharing from ordinary collaboration prose.
Canceling a handoff
Cancel only when the user asks to discard a handoff or you created one by mistake. Use the exact handoff id returned by the begin tool. Cancellation is idempotent from the user's point of view, but it should still target only the known handoff.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago Changed · +5 lines a8836b7d1737
- 11d ago First seen · 40 lines · 57 tokens per session scan A 70116ed91f71
ai-memory-handoff is a skill published in the GitHub repository akitaonrails/ai-memory (6,432 stars, last pushed today), licensed MIT. It adds 57 tokens to every session and 772 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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